The present invention relates generally to machine diagnostics, and more specifically, to a system and method for sorting machine data, e.g., operational parameter data and incident log data to facilitate analysis of one or more machines undergoing diagnostics.
A machine, such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiences-or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively little pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair.
CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or xe2x80x9cgold standardxe2x80x9d cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach which uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs are grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. Pat. No. 6,343,236, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. Pat. No. 6,415,395, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations.
It is believed that the diagnostic tools disclosed in the foregoing patent applications provide substantial advantages and advancements in the art of diagnostics. It would be desirable, however, to provide a system and method that allows for sorting the incident log data based on the severity of the incident so that incidents likely to result in a mission failure, e.g., a road failure in the case of a locomotive, are prioritized over incidents not likely to affect machine operation, at least for a predetermined period of time. It would be further desirable, through the use of incident-authentication rules, to substantially eliminate nuisance incidents, e.g., incidents containing unreliable or useless information that may be present in the incident log data and/or the operational parameter data. This sorting or triage process would advantageously allow for increasing the probability of early detection of actual incipient failures in the machine, as well as decreasing the probability of falsely declaring non-existent failures. Further, such incident-authentication rules would prevent feeding the nuisance incidents into the diagnostic tools and would thus further enhance the predictive accuracy of the diagnostic algorithms used therein while conserving the processing power consumed by the diagnostic algorithms.
Generally speaking, one embodiment of the present invention fulfills the foregoing needs by providing a method for sorting respective incident log data from a plurality of machines undergoing diagnostics. The method allows for receiving incident log data comprising one or more incidents from the plurality of machines. The method further allows for assigning a predetermined incident severity rating to the respective incidents and for processing the respective incidents based on their respective incident severity rating.
Another embodiment of the present invention further fulfills the foregoing needs by providing a method for sorting respective incident log data from a plurality of machines undergoing diagnostics. The method allows for receiving incident log data comprising one or more incidents from the plurality of machines and for receiving operational parameter data from the plurality of machines. The method further allows for executing a set of incident-authentication rules indicative of whether a received incident actually corresponds to a faulty condition or not. An assigning step allows for assigning a predetermined incident severity rating to the respective incidents, and a processing step allows for processing the respective incidents based on their respective incident severity rating.
The present invention further provides a system for sorting respective incident log data from a plurality of machines undergoing diagnostics. The system includes means for receiving incident log data comprising one or more incidents from the plurality of machines. The system further includes means for assigning a predetermined incident severity rating to the respective incidents, and means for processing the respective incidents based on their respective incident severity rating.